Determining whether to take an action by applying a metric calculated using natural language processing tokens

ABSTRACT

Embodiments described herein are directed to computer-implemented methods, systems, and computer program products for calculating a metric using natural language processing tokens. A non-limiting example of the computer-implemented method includes parsing, by a processing device, user content using a natural language processing technique to extract tokens. The method further includes filtering, by the processing device, the tokens relating to a natural language processing criterion. The method further includes calculating, by the processing device, a metric based at least in part on the filtered tokens. The method further includes determining, by the processing device, whether to take an action by applying the metric to a set of rules. The method further includes taking the action responsive to determining to take the action.

This application is a continuation of U.S. patent application Ser. No.15/701,704, entitled “DETERMINING WHETHER TO TAKE AN ACTION BY APPLYINGA METRIC CALCULATED USING NATURAL LANGUAGE PROCESSING TOKENS,” filedSep. 12, 2017, the disclosure of which is incorporated by referenceherein in its entirety.

BACKGROUND

The present disclosure generally relates to natural language processing,and more specifically, to using a set of rules to determine whether totake an action by applying a metric that is calculated using naturallanguage processing tokens.

Natural language processing (NLP) enables computing devices tounderstand human language. NLP enables computing devices to analyze,understand, and derive meaning from human text/language. For example, acomputing device equipped with NLP can receive a user question posed innatural (human) language, process the question (e.g., NLP, informationretrieval, knowledge representation, automated reasoning, and machinelearning), and return results to the user.

SUMMARY

Embodiments described herein are directed to computer-implementedmethods, systems, and computer program products for calculating a metricusing natural language processing tokens. A non-limiting example of thecomputer-implemented method includes parsing, by a processing device,user content using a natural language processing technique to extracttokens. The method further includes filtering, by the processing device,the tokens relating to a natural language processing criterion. Themethod further includes calculating, by the processing device, a metricbased at least in part on the filtered tokens. The method furtherincludes determining, by the processing device, whether to take anaction by applying the metric to a set of rules. The method furtherincludes taking the action responsive to determining to take the action.

Additional technical features and benefits are realized through thetechniques of the present disclosure. Embodiments and aspects of thepresent disclosure are described in detail herein and are considered apart of the claimed subject matter. For a better understanding, refer tothe detailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments described herein are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to aspects of thepresent disclosure;

FIG. 2 depicts abstraction model layers according to aspects of thepresent disclosure;

FIG. 3 depicts a processing system for implementing the techniquesdescribed herein according to aspects of the present disclosure;

FIG. 4 depicts a block diagram of a cloud processing system for using aset of rules to determine whether to take an action by applying a metricthat is calculated using natural language processing tokens according toaspects of the present disclosure;

FIG. 5 depicts a flow diagram of a method for using a set of rules todetermine whether to take an action by applying a metric that iscalculated using natural language processing tokens according to aspectsof the present disclosure;

FIG. 6 depicts a flow diagram of a method for using a set of rules todetermine whether to take an action by applying a metric that iscalculated using natural language processing tokens according to aspectsof the present disclosure; and

FIG. 7 depicts a graphical user interface for displaying product safetyinformation according to aspects of the present disclosure.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagrams or the operations described therein withoutdeparting from the spirit of the embodiments described herein. Forinstance, the actions can be performed in a differing order or actionscan be added, deleted or modified. Also, the term “coupled” andvariations thereof describes having a communications path between twoelements and does not imply a direct connection between the elementswith no intervening elements/connections between them. All of thesevariations are considered a part of the specification.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers.

DETAILED DESCRIPTION

Various embodiments are described herein with reference to the relateddrawings. Alternative embodiments can be devised without departing fromthe scope of this disclosure. Various connections and positionalrelationships (e.g., over, below, adjacent, etc.) are set forth betweenelements in the following description and in the drawings. Theseconnections and/or positional relationships, unless specified otherwise,can be direct or indirect, and the present disclosure is not intended tobe limiting in this respect. Accordingly, a coupling of entities canrefer to either a direct or an indirect coupling, and a positionalrelationship between entities can be a direct or indirect positionalrelationship. Moreover, the various tasks and process steps describedherein can be incorporated into a more comprehensive procedure orprocess having additional steps or functionality not described in detailherein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” may be understood to include any integer numbergreater than or equal to one, i.e. one, two, three, four, etc. The terms“a plurality” may be understood to include any integer number greaterthan or equal to two, i.e. two, three, four, five, etc. The term“connection” may include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the present disclosure may or may not be described indetail herein. In particular, various aspects of computing systems andspecific computer programs to implement the various technical featuresdescribed herein are well-known. Accordingly, in the interest ofbrevity, many conventional implementation details are only mentionedbriefly herein or are omitted entirely without providing the well-knownsystem and/or process details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of thepresent disclosure are not limited thereto. As depicted, the followinglayers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and product safety rating and notification96.

It is understood that embodiments of the present disclosure are capableof being implemented in conjunction with any other suitable type ofcomputing environment now known or later developed. For example, FIG. 3illustrates a block diagram of a processing system 300 for implementingthe techniques described herein. In examples, processing system 300 hasone or more central processing units (processors) 321 a, 321 b, 321 c,etc. (collectively or generically referred to as processor(s) 321 and/oras processing device(s)). In aspects of the present disclosure, eachprocessor 321 may include a reduced instruction set computer (RISC)microprocessor. Processors 321 are coupled to system memory (e.g.,random access memory (RAM) 324) and various other components via asystem bus 333. Read only memory (ROM) 322 is coupled to system bus 333and may include a basic input/output system (BIOS), which controlscertain basic functions of processing system 300.

Further illustrated are an input/output (I/O) adapter 327 and acommunications adapter 326 coupled to system bus 333. I/O adapter 327may be a small computer system interface (SCSI) adapter thatcommunicates with a hard disk 323 and/or a tape storage drive 325 or anyother similar component. I/O adapter 327, hard disk 323, and tapestorage device 325 are collectively referred to herein as mass storage334. Operating system 40 for execution on processing system 300 may bestored in mass storage 334. A network adapter 326 interconnects systembus 333 with an outside network 336 enabling processing system 300 tocommunicate with other such systems.

A display (e.g., a display monitor) 335 is connected to system bus 333by display adaptor 332, which may include a graphics adapter to improvethe performance of graphics intensive applications and a videocontroller. In one aspect of the present disclosure, adapters 326, 327,and/or 332 may be connected to one or more I/O busses that are connectedto system bus 333 via an intermediate bus bridge (not shown). SuitableI/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 333via user interface adapter 328 and display adapter 332. A keyboard 329,mouse 330, and speaker 31 may be interconnected to system bus 33 viauser interface adapter 328, which may include, for example, a Super I/Ochip integrating multiple device adapters into a single integratedcircuit.

In some aspects of the present disclosure, processing system 300includes a graphics processing unit 337. Graphics processing unit 337 isa specialized electronic circuit designed to manipulate and alter memoryto accelerate the creation of images in a frame buffer intended foroutput to a display. In general, graphics processing unit 337 is veryefficient at manipulating computer graphics and image processing and hasa highly parallel structure that makes it more effective thangeneral-purpose CPUs for algorithms where processing of large blocks ofdata is done in parallel.

Thus, as configured herein, processing system 300 includes processingcapability in the form of processors 321, storage capability includingsystem memory (e.g., RAM 324), and mass storage 334, input means such askeyboard 329 and mouse 330, and output capability including speaker 331and display 335. In some aspects of the present disclosure, a portion ofsystem memory (e.g., RAM 324) and mass storage 334 collectively store anoperating system such as the AIX® operating system from IBM Corporationto coordinate the functions of the various components shown in theprocessing system 300.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the present disclosure, the present techniquesprovide for using a set of rules to determine whether to take an actionby applying a metric that is calculated using natural languageprocessing tokens. For example, user content is parsed using naturallanguage processing techniques to extract tokens. The tokens that relateto a natural language processing criterion are filtered, and a metric iscalculated based on the filtered tokens. It is then determined whetherto take an action by applying the metric to a set of rules.

As an example, content-based product safety ratings can be generatedusing natural language processing of user content. The user content caninclude, for example, product ratings, product reviews, social mediaposts about the product, video reviews, etc. Consumers can purchase orotherwise acquire products and services, such as from brick-and-mortarstores and/or online virtual stores using e-commerce systems. Variousproducts and services can also have ratings and reviews associatedtherewith. These ratings and reviews can be provided, for example, byother consumers or experts and can be useful when deciding whether tomake a purchase

Traditionally, a seemingly innocent product can have a dangerous designflaw, quality control issue, and/or defect that can cause sickness,injury, or even fatality. For example, pet foods or pet treats may bepoorly regulated and can contain quality and safety defects. Similarly,poorly designed products such as poorly designed infant seats or poolfloaties have also been linked to injury and fatality.

Despite the occurrence of damage, sickness, injury, death, etc., theseproducts are not always recalled in a timely manner. Some products couldeven remain on shelves or in warehouses of retailers despite a recall.Moreover, some products may not even be recalled due to insufficientevidence or a failure or unwillingness of the manufacturer to issue arecall.

Although product reviews and ratings may already exist online, it isimpractical for a user to search through all these reviews to identifypotential safety issues. For example, searching through many hundreds oreven thousands of reviews can be time-consuming and inefficient.Moreover, some consumers do not bother researching smaller or habitualpurchases. Even when research is performed, it can be difficult for aconsumer to determine which reviews should be taken more seriously dueto uncertainty in the expertise level of the reviewer (author).

Turning now to an overview of present techniques, one or moreembodiments described herein address the above-described shortcomings ofthe prior art by using natural language processing techniques to parseuser content to generate tokens. The tokens can then be used tocalculate a metric, to which a set of rules can be applied to determinewhether to take an action. These techniques can be applied to identifysafety issues with a product of interest. In particular, the presenttechniques parse user content such as reviews, articles, social mediaposts, videos, etc., for a product of interest to identify informationrelated to product safety of the product of interest. The user contentis generally provided by other consumers (referred to as “authors”) whohave purchased or used the particular product. Using results of theparsing, a metric (e.g., a product safety score) is calculated, andspecific portions of the user content related to product safety (e.g.,text describing a product defect) can be stored and made available to auser (e.g., a potential purchaser). The metric can be applied to a setof rules to determine whether to take an action, and the action (e.g.,issuing a recall, suggesting a similar product, etc.) can be taken.

In addition, a reputation factor can be computed for the authors of theuser content. For example, a review authored by a “verified expert”(e.g., a doctor, a veterinarian, etc.) can be weighted higher than areview by a non-verified expert. As another example, reviewers that havewritten a high number of reviews with positive reaction can also receivefavorable reputation factor scores and can be weighted higher than userswho have not written a higher number of reviews. The reputation factorcan aid a consumer in determining whether a review is trustworthy.

To access the safety information, a consumer can search for a product(e.g., by name, by photograph, by barcode, by model number, etc.). Inaddition, a database of products that a consumer owns/uses can bemaintained, and the user can be notified if any of the products in thedatabase experience an increase in product safety score (such as above athreshold).

Turning now to a more detailed description of aspects of the presentdisclosure, FIG. 4 depicts a block diagram of a cloud processing system400 for using a set of rules to determine whether to take an action byapplying a metric that is calculated using natural language processingtokens according to aspects of the present disclosure. The cloudprocessing system 400 can be implemented in the cloud computingenvironment 50, for example.

The cloud processing system 400 includes a data store 402 that storescontent (e.g., content about products) that is received from authors 401(e.g., Author 1, Author 2, Author i, etc.). In some examples, thecontent can be retrieved from other data stores (e.g., product reviewsstored on e-commerce platforms, posts stored on social media platforms,etc.). The content 406 can be written product reviews, video productreviews, verbal product reviews, product forums, social media posts, andthe like. The data store 402 can be continuously updated by authors, byweb crawlers searching for new content, etc.

The cloud processing system 400 also includes an analysis module 404that can be used to find and parse the content to search for and extractproduct information, such as information relating to product safety. Theanalysis module 404 includes an NLP engine 410, a content analysisengine 412, a reputation factor engine 414, and a product safety scoreengine 416.

The NLP engine 410 can include various application programminginterfaces (APIs) to find and parse user content relating to productsand extract product safety information, for example, from the usercontent. The following are examples of some APIs that can be implementedin the NLP engine 410: an AlchemyLanguage API to use NLP and machinelearning algorithms to analyze text; a Natural Language Classifier APIto use machine learning algorithms to return matching predefined classesfor short text inputs; a Personality Insights API to use linguisticanalytics to infer individuals' intrinsic personality characteristics toderive insights from social media posts, enterprise data, or otherdigital communications; a Tone Analyzer API to use linguistic analysisto detect types of tones from written text, such as emotions (e.g.,anger, cheerfulness, sadness, etc.), social tendencies, and languagestyle; a Speech to Text API to transcribe speech from various languagesand audio formats to text with low latency; a Visual Recognition API touse deep learning algorithms to identify scenes, objects, and faces inimages; an AlchemyData News API to use natural language processingtechniques to query news and blogs; and/or a Tradeoff Analytics API tohelp make decisions when faced with a decision problem that includesmultiple goals and alternatives (e.g., to explore trade-offs betweenoptions to make complex decisions).

Using these APIs, the NLP engine 410 parses the content in the datastore to search for information relating to a product of interest, forexample. When product information is found, the content analysis engine412 analyzes the content about the product of interest based on tone,context, credibility, etc. using the APIs. For example, content isanalyzed by searching for tokens related to product safety (e.g.,positive safety information and negative safety information). Tokenssuch as “sick,” “injure,” “pain,” “disease,” “concussion,” etc., may beconsidered by the content analysis engine 412 to be negative safetyinformation, for example. Further text processing is then performed bythe content analysis engine 412 to understand the context of theidentified token to determine whether the reviewed product is causingnegative symptoms (e.g., “unsafe for children” or “caused vomiting” areconsidered negative safety information”) or contains positive safetyinformation (e.g., “reduces pain” or “make the user feel less nauseous”are considered positive safety information even though they containwords like “pain” and “nauseous”). Tokens such as “secure,” “safe,”“reinforced,” “stable,” etc. may be considered by the content analysisengine 412 to be positive safety information. Further text processing isperformed to understand the context of the identified token to ensurethat the reviewed product is the reason for the positive sentiment.Content that is analyzed that contains no safety information (eitherpositive or negative) can be discarded (e.g., content that discusses howa product works, shipping information for the product, etc.).

Based on the analysis by the content analysis engine 412, a productsafety metric (or “score”) can be calculated by the product safety scoreengine 416. The metric can be based, for example, on a number ofnegative safety information occurrences versus a number of positivesafety information occurrences. In another example, the metric can bebased on a number of negative safety information occurrences versus anumber of total reviews. For example, two negative safety informationoccurrences for 1,500 reviews may receive a relatively high safetymetric (indicating a relatively safe product) while nine negative safetyinformation occurrences for 30 reviews may receive a relatively lowsafety metric (indicating a potentially unsafe product). The metric canbe numerical in nature (e.g., a number on a scale of 1 to 10, apercentage of negative safety information, etc.) or categorical (e.g.,low/medium/high, safe/unsafe, etc.). In yet another example, theseverity of the review may also be considered. For example, a productreview that indicates a fatality could weigh more heavily than anotherreview that indicates a minor injury.

The product safety score engine 416 can refine the metric using areputation factor determined by the reputation factor engine 414. Forexample, the reputation factor engine 414 can take into account areputation of the author of the content. A user that has hundreds ofpositive reviews may indicate a high reputation factor score, andcontent from this user may increase a products' safety metric.Favorability of a user's content can be judged based on how helpful theuser's reviews are judged to be. For example, many review sites enablevoting on reviews to indicate whether the review is helpful orunhelpful. If the user has multiple reviews in a certain productcategory (e.g., over the counter drugs) that are consistently favorable,the user's reputation in that product group may be higher than theuser's reputation in another product group.

In another example, a recognized (verified) expert may have a higherreputation factor score, and content from this user may also increase aproducts' safety metric if the content relates to the user's expertise.For example, a veterinarian may be considered an expert on pet food, adoctor may be considered an expert on over the counter drugs, etc.Expert advice towards a product, whether positive or negative, is givena higher weight in deciding safety ratings of a product of interest. Anexpert can be verified, for example, by providing a copy of theirlicense or degree, etc.

The reputation factor engine 414 calculates a reputation factor for eachauthor based on the factors listed above. The reputation factor can bemultiplied by the product safety metric calculated by the product safetyscore engine 416 to form a final product safety score. As the reputationof the user increases, the final product safety score can changeaccordingly (i.e., increase or decrease). The final product safety scorecan be used to initiate an action (e.g., suggest a similar, saferproduct, issue a product recall, send a warning to users about theproduct of interest, etc.).

According to aspects of the present disclosure, a database of a user'spurchased products can be maintained. The present techniques can be usedto search for content relating to safety for the user's purchasedproducts, and the user can be warned if new content is published thatwould indicate negative safety information, for example.

FIG. 5 depicts a flow diagram of a method 500 for using a set of rulesto determine whether to take an action by applying a metric that iscalculated using natural language processing tokens according to aspectsof the present disclosure. The method 500 can be performed by anysuitable processing device or system, such as the processing system 300of FIG. 3 or the cloud processing system 400 of FIG. 4.

At block 502, the analysis module 404 of the processing device parsesuser content using a natural language processing technique to extracttokens. According to an example of the present disclosure, the usercontent can relate to a product of interest. The user content can be inthe form of product reviews, product ratings, social media posts,published articles, television and radio content, and the like.

At block 504, the analysis module 404 of the processing device filtersthe tokens relating to a natural language processing criterion. Forexample, in the case of the user content relating to a product ofinterest, the tokens can be filtered using an NLP criterion relating toproduct safety (e.g., injuries, sickness, fatalities, etc.) for theproduct of interest. In the case of the user content relating to aproduct of interest, the content can be parsed to extract tokens thatrelate to product safety (e.g., injuries, sickness, fatalities, etc.)for the product of interest.

At block 506, the analysis module 404 of the processing devicecalculates a metric based at least in part on the filtered tokens. Inthe product safety example, the metric (or “score”) is a safety metricand can be calculated based on safety-related tokens appearing in thecontent. For example, a product of interest with many reviews thatinclude tokens relating to poor product safety may result in a lowerproduct safety score.

In additional examples, the metric can be updated based on a reputationfactor of an author of the content, as described herein.

At block 508, the analysis module 404 of the processing devicedetermines whether to take an action by applying the metric to a set ofrules. According to aspects of the present disclosure, the set of rulescan use thresholds to determine whether to take an action. For example,if a product safety score is lower than a first threshold, a differentproduct that is similar may be recommended to the user. In anotherexample, if the product safety score is lower than a second threshold, aproduct recall may be needed.

At block 510, an action can be taken when it is determined to take anaction. For example, the action can include initiating a product recallfor a product of interest, recommending a different product similar tothe product of interest, or another suitable action.

Additional processes also may be included, and it should be understoodthat the processes depicted in FIG. 5 represent illustrations and thatother processes may be added or existing processes may be removed,modified, or rearranged without departing from the scope and spirit ofthe present disclosure.

FIG. 6 depicts a flow diagram of a method 600 for using a set of rulesto determine whether to take an action by applying a metric that iscalculated using natural language processing tokens according to aspectsof the present disclosure. The method 500 can be performed by anysuitable processing device or system, such as the processing system 300of FIG. 3 or the cloud processing system 400 of FIG. 4.

At block 602, the analysis module 404 of the processing device searchesfor product content (e.g., user content about a product of interest),such as in the data store 402. At decision block 604, the analysismodule 404 determines whether the product content discusses productsafety. If not, the method 600 returns to search for additional productcontent at block 602. However, if at decision block 604 it is determinedthat the content discusses product safety for the product of interest, aproduct safety score is calculated at block 606 for the product ofinterest. The safety score can be based, for example, on a user'sreaction to the product content (e.g., the severity of an injury).

At decision block 608, it is determined whether the product content forthe product of interest is authored by a recognized author. Recognizedauthors can be habitual reviewers, recognized experts (e.g., a doctor),or another trusted source. If at decision block 608 it is determinedthat the author is not recognized, a new author profile is created atblock 610, and the author's profile is assigned a baseline reputationfactor (e.g., a reputation factor of “1”). If, however, at decisionblock 608 it is determined that the author is recognized, the author'sprofile can be updated based on how helpful/useful the content is atblock 612.

At block 614, the content score for the content is multiplied by theauthor's reputation factor to generate a safety score. In this way,content generated by recognized authors can be weighted eitherpositively or negatively depending on the author (e.g., a medical expertmay have a higher reputation factor, an author known for writing fakereviews may have a lower reputation factor). At block 616, an overallproduct safety score is updated based on the safety score.

Additional processes also may be included, and it should be understoodthat the processes depicted in FIG. 6 represent illustrations and thatother processes may be added or existing processes may be removed,modified, or rearranged without departing from the scope and spirit ofthe present disclosure.

FIG. 7 depicts a graphical user interface (GUI) 700 for displayingproduct safety information according to aspects of the presentdisclosure. The GUI 700 can be displayed on a display of a processingdevice 701 or system such as the display 335 of the processing system300. According to the example of FIG. 7, the GUI 700 includes a productsafety score region 702 to present a product safety score to a user. TheGUI 700 also includes a review region 704 to present review information,such as portions (i.e., snippets) of reviews that support the productsafety score. The GUI 700 can also include an alternative product region706 to present alternative products that can be selected and presentedbased on their respective product safety scores. For example, afunctionally similar product with a higher product safety score may bepresented as one alternative, while a less expensive product with ahigher product safety score may be presented as another alternative.

The present disclosure may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure.

In this regard, each block in the flowchart or block diagrams mayrepresent a module, segment, or portion of instructions, which comprisesone or more executable instructions for implementing the specifiedlogical function(s). In some alternative implementations, the functionsnoted in the blocks may occur out of the order noted in the Figures. Forexample, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method comprising:parsing, by a processing device, user content using a natural languageprocessing technique to extract tokens; filtering, by the processingdevice, the tokens relating to a natural language processing criterion;calculating, by the processing device, a metric based at least in parton the filtered tokens; determining, by the processing device, whetherto take an action by applying the metric to a set of rules; and takingthe action responsive to determining to take the action.
 2. Thecomputer-implemented method of claim 1, wherein the user content relatesto a product of interest.
 3. The computer-implemented method of claim 2,wherein the metric is a safety metric for the product of interest. 4.The computer-implemented method of claim 2, wherein the action comprisesinitiating a product recall for the product of interest.
 5. Thecomputer-implemented method of claim 2, wherein the action comprisesrecommending a different product similar to the product of interest. 6.The computer-implemented method of claim 5, wherein the metric is afirst safety metric, wherein the first safety metric is associated withthe product of interest, and wherein a second safety metric isassociated with the different product.
 7. The computer-implementedmethod of claim 6, wherein the second safety metric is greater than thefirst safety metric.
 8. The computer-implemented method of claim 1,further comprising: calculating, by the processing device, a reputationfactor for an author of the user content; and recalculating, by theprocessing device, the metric based at least in part on the reputationfactor.
 9. The computer-implemented method of claim 1, wherein the setof rules utilizes a plurality of thresholds to determine whether to takethe action.
 10. The computer-implemented method of claim 9, wherein theplurality of thresholds comprises a first threshold that is less than asecond threshold, wherein it is determined to take a first action whenthe metric is greater than the first threshold and less than the secondthreshold, and wherein it is determined to take a second action when themetric is greater than the first threshold and the second threshold.